Skip to main content

Custom PyMC3 models built on top of the scikit-learn API

Project description

PyMC3 Models

Custom PyMC3 models built on top of the scikit-learn API. Check out the docs.


  • Reusable PyMC3 models including LinearRegression and HierarchicalLogisticRegression
  • A base class, BayesianModel, for building your own PyMC3 models


The latest release of PyMC3 Models can be installed from PyPI using pip:

pip install pymc3_models

The current development branch of PyMC3 Models can be installed from GitHub, also using pip:

pip install git+

To run the package locally (in a virtual environment):

git clone
cd pymc3_models
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt


Since PyMC3 Models is built on top of scikit-learn, you can use the same methods as with a scikit-learn model.

from pymc3_models import LinearRegression

LR = LinearRegression(), Y)
LR.score(X, Y)


For more info, see CONTRIBUTING.

Contributor Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See CODE_OF_CONDUCT.


This library is built on top of PyMC3 and scikit-learn.


Apache License, Version 2.0

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pymc3-models, version 2.0.0
Filename, size File type Python version Upload date Hashes
Filename, size pymc3_models-2.0.0.tar.gz (15.6 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page